Variability of effects of spatial climate data aggregation on regional yield simulation by crop models-猫眼课题宝

DOI：

10.3354/cr01326

发表时间：

2015-09

期刊：

Climate Research

影响因子：

1.1

通讯作者：

中科院分区：

地球科学4区

文献类型：

作者：

H. Hoffmann;Gang Zhao;L. V. Bussel;Andreas Enders;X. Specka;C. Sosa;J. Yeluripati;F. Tao;J. Constantin;H. Raynal;E. Teixeira;B. Grosz;L. Doro;Zhigan Zhao;E. Wang;C. Nendel;K. Kersebaum;E. Haas;R. Kiese;S. Klatt;H. Eckersten;E. Vanuytrecht;M. Kuhnert;E. Lewan;R. Rötter;P. Roggero;D. Wallach;D. Cammarano;S. Asseng;Gunther Krauss;S. Siebert;T. Gaiser;F. Ewert

农田尺度作物模型通常应用于比耕地更粗糙的空间分辨率。然而，对于这些模式对空间聚合的气候输入数据的响应以及这些响应在不同模式之间可能存在差异的原因，我们知之甚少。根据不同的模式，与高分辨率输入数据的模拟相比，大规模模拟的区域产量估计值可能存在偏差。我们对德国北莱茵-威斯特伐利亚地区的13种作物模型评估了这种所谓的聚集效应。这些模式提供了1 km分辨率的气候数据和高达100 km分辨率的栅格空间聚集体。该模型以2种作物（冬小麦和青贮玉米）和3种生产情况（潜力、限水和氮水限制生长）为对象，以提高对模型模拟中与数据聚集相关的误差以及与模型结构可能存在的相互作用的理解。在确定模拟产量的特定模式输入数据汇总时确定的最重要的气候变量主要与辐射（小麦）和温度（玉米）的变化有关。此外，无论效应的程度如何，聚集效应都是系统性的。气候输入数据汇总使平均模拟区域产量变化高达0.2 t ha-1，而单年和模式的模拟产量差异很大，这取决于数据汇总。这意味着大规模作物产量模拟对气候数据的聚合是稳健的。然而，根据模式及其参数化，在更高的时间或空间分辨率下进行评估时，大尺度模拟可能存在系统偏差。

Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha-1, whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

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